Method for fast compression of program codes for OTA updation in consumer devices

BACKGROUND Underuse of hearing aids is caused by several factors, including the stigma associated with hearing disability, affordability, and lack of awareness of rising hearing impairment associated with the growing population. Thus, there is a significant opportunity for the development of direct-to-consumer devices. For the past few years, smartphone-based hearing-aid apps have become more numerous and diverse, but few studies have investigated them. OBJECTIVE This study aimed to elucidate the electroacoustic characteristics and potential user benefits of a selection of currently available hearing-aid apps. METHODS We investigated the apps based on hearing-aid control standards (American National Standards Institute) using measurement procedures from previous studies. We categorized the apps and excluded those we considered inefficient. We investigated a selection of user-friendly, low-end apps, EarMachine and Sound Amplifier, with warble-tone audiometry, word recognition testing in unaided and aided conditions, and hearing-in-noise test in quiet and noise-front conditions in a group of users with mild hearing impairment (n = 7) as a pilot for a future long-term investigation. Results from the apps were compared with those of a conventional hearing aid. RESULTS Five of 14 apps were considered unusable based on low scores in several metrics, while the others varied across the range of electroacoustic measurements. The apps that we considered “high end” that provided lower processing latencies and audiogram-based fitting algorithms were superior overall. The clinical performance of the listeners tended to be better when using hearing aid, while the low end hearing-aid apps had limited benefits on the users. CONCLUSIONS Some apps showed the potential to benefit users with limited cases of minimal or mild hearing loss if the inconvenience of relatively poor electroacoustic performance did not outweigh the benefits of amplification.

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Virtualizing AI at the Distributed Edge Towards Intelligent IoT Applications

Journal of Sensor and Actuator Networks ◽

10.3390/jsan10010013 ◽

2021 ◽

Vol 10 (1) ◽

pp. 13

Author(s):

Claudia Campolo ◽

Giacomo Genovese ◽

Antonio Iera ◽

Antonella Molinaro

Keyword(s):

Resource Constraints ◽

Smart Cities ◽

Traditional Approach ◽

Low Cost ◽

Iot Applications ◽

Software And Hardware ◽

Iot Devices ◽

Consumer Devices ◽

Hardware Platforms ◽

Smart Factories

Several Internet of Things (IoT) applications are booming which rely on advanced artificial intelligence (AI) and, in particular, machine learning (ML) algorithms to assist the users and make decisions on their behalf in a large variety of contexts, such as smart homes, smart cities, smart factories. Although the traditional approach is to deploy such compute-intensive algorithms into the centralized cloud, the recent proliferation of low-cost, AI-powered microcontrollers and consumer devices paves the way for having the intelligence pervasively spread along the cloud-to-things continuum. The take off of such a promising vision may be hurdled by the resource constraints of IoT devices and by the heterogeneity of (mostly proprietary) AI-embedded software and hardware platforms. In this paper, we propose a solution for the AI distributed deployment at the deep edge, which lays its foundation in the IoT virtualization concept. We design a virtualization layer hosted at the network edge that is in charge of the semantic description of AI-embedded IoT devices, and, hence, it can expose as well as augment their cognitive capabilities in order to feed intelligent IoT applications. The proposal has been mainly devised with the twofold aim of (i) relieving the pressure on constrained devices that are solicited by multiple parties interested in accessing their generated data and inference, and (ii) and targeting interoperability among AI-powered platforms. A Proof-of-Concept (PoC) is provided to showcase the viability and advantages of the proposed solution.

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Robust DTW-based recognition algorithm for hand-held consumer devices [speech recognition]

2005 Digest of Technical Papers. International Conference on Consumer Electronics, 2005. ICCE. ◽

10.1109/icce.2005.1429903 ◽

2005 ◽

Cited By ~ 1

Author(s):

Chanwoo Kim ◽

Kwang-deok Seo

Keyword(s):

Speech Recognition ◽

Recognition Algorithm ◽

Consumer Devices

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Design, Reliability and Manufacturing Readiness for MEMS Microphone

ASME 2011 Pacific Rim Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Systems, MEMS and NEMS: Volume 1 ◽

10.1115/ipack2011-52002 ◽

2011 ◽

Cited By ~ 1

Author(s):

Ganesh Iyer ◽

Wei Li ◽

Lavanya Gopalakrishnan

Keyword(s):

Electrical Signal ◽

Consumer Products ◽

Smart Phones ◽

System Level ◽

Design Considerations ◽

Final Assembly ◽

Mems Microphone ◽

Robust System ◽

Manufacturing Readiness ◽

Consumer Devices

Microphone is a critical component for seamless communication converting an acoustic signal (vocal) to an electrical signal. Traditionally Electrets Condenser Microphones (ECM) have been the primary proponent of audio component in many consumer products. With functionally rich consumer devices (example smart phones, etc) there is a growing trend to look at components with higher functionality but a smaller form factor. Microelectronic Mechanical Systems (MEMS) microphone is seen as a possible replacement to ECM due to its significant reduction in form fit with additional functionality. The paper is an effort to illustrate steps that can be considered while designing MEMS microphone in a system. This includes Design considerations, Reliability tests, Manufacturing challenges and Readiness to ensure higher yield during the final assembly. Manufacturing issues (Top 5) and guideline presented in the paper are not just to increase the assembly yield (system level), but also to increase an awareness upfront to the design phase to help create a robust system/product.

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Sleep and BMI: Do (Fitbit) bands aid?

F1000Research ◽

10.12688/f1000research.14774.2 ◽

2018 ◽

Vol 7 ◽

pp. 511 ◽

Cited By ~ 1

Author(s):

Laura McDonald ◽

Faisal Mehmud ◽

Sreeram V. Ramagopalan

Keyword(s):

Body Mass Index ◽

Body Mass ◽

Biomedical Research ◽

Chinese Population ◽

Scientific Research ◽

Negatively Associated ◽

Significant Potential ◽

Consumer Devices

Recent studies have used mainstream consumer devices (Fitbit) to assess sleep objectively and test the well documented association between sleep and body mass index (BMI). In order to further investigate the applicability of Fitbit data for biomedical research across the globe, we analysed openly available Fitbit data from a largely Chinese population. We found that after adjusting for age, gender, race, and average number of steps taken per day, average hours of sleep per day was negatively associated with BMI (p=0.02), further demonstrating the significant potential for wearables in international scientific research.

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Effects of Contact Pressure in Reflectance Photoplethysmography in an In Vitro Tissue-Vessel Phantom

Sensors ◽

10.3390/s21248421 ◽

2021 ◽

Vol 21 (24) ◽

pp. 8421

Author(s):

James M May ◽

Elisa Mejía-Mejía ◽

Michelle Nomoni ◽

Karthik Budidha ◽

Changmok Choi ◽

...

Keyword(s):

Contact Pressure ◽

Morphological Analysis ◽

Signal To Noise Ratio ◽

In Vitro Study ◽

Wearable Technologies ◽

Sensor Device ◽

Artificial Tissue ◽

Optimum Sensor ◽

Consumer Devices

With the continued development and rapid growth of wearable technologies, PPG has become increasingly common in everyday consumer devices such as smartphones and watches. There is, however, minimal knowledge on the effect of the contact pressure exerted by the sensor device on the PPG signal and how it might affect its morphology and the parameters being calculated. This study explores a controlled in vitro study to investigate the effect of continually applied contact pressure on PPG signals (signal-to-noise ratio (SNR) and 17 morphological PPG features) from an artificial tissue-vessel phantom across a range of simulated blood pressure values. This experiment confirmed that for reflectance PPG signal measurements for a given anatomical model, there exists an optimum sensor contact pressure (between 35.1 mmHg and 48.1 mmHg). Statistical analysis shows that temporal morphological features are less affected by contact pressure, lending credit to the hypothesis that for some physiological parameters, such as heart rate and respiration rate, the contact pressure of the sensor is of little significance, whereas the amplitude and geometric features can show significant change, and care must be taken when using morphological analysis for parameters such as SpO2 and assessing autonomic responses.

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Framework for Selecting and Benchmarking Mobile Devices in Psychophysiological Research

10.31234/osf.io/a9ju4 ◽

2019 ◽

Cited By ~ 2

Author(s):

Ian R Kleckner ◽

Mallory Feldman ◽

Matthew Goodwin ◽

Karen S. Quigley

Keyword(s):

Physical Activity ◽

Consumer Electronics ◽

Use Cases ◽

Assessment Procedure ◽

Laboratory System ◽

Activity Data ◽

Physiological Measurement ◽

Physical Activity Data ◽

Quantitative Analyses ◽

Consumer Devices

Commercially available consumer electronics (smartwatches and wearable biosensors) are increasingly enabling acquisition of peripheral physiological and physical activity data inside and outside of laboratory settings. However, there is scant literature available for selecting and assessing the suitability of these novel devices for scientific use. To overcome this limitation, the current paper offers a framework to aid researchers in choosing and evaluating wearable technologies for use in empirical research. Our seven-step framework includes: (1) identifying signals of interest; (2) characterizing intended use cases; (3) identifying study-specific pragmatic needs; (4) selecting devices for evaluation; (5) establishing an assessment procedure; (6) performing qualitative and quantitative analyses on resulting data; and, if desired, (7) conducting power analyses to determine sample size needed to more rigorously compare performance across devices. We illustrate the application of the framework by comparing electrodermal, cardiovascular, and accelerometry data from a variety of commercial wireless sensors (Affectiva Q, Empatica E3, Empatica E4, Actiwave Cardio, Shimmer) relative to a well-validated, wired Mindware laboratory system. Our evaluations are performed in two studies (N=10, N=11) involving psychometrically sound, standardized tasks that include physical activity and affect induction. After applying our framework to this data, we conclude that only some commercially available consumer devices for physiological measurement are capable of wirelessly measuring peripheral physiological and physical activity data of sufficient quality for scientific use cases. Thus, the framework appears to be beneficial at suggesting steps for conducting more systematic, transparent, and rigorous evaluations of mobile physiological devices prior to deployment in studies.

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